Question

有没有办法压缩任意嵌套的Spark Dataframe？我所看到的大部分工作都是针对特定架构编写的，并且我希望能够通过不同的嵌套类型（例如StructType，ArrayType，MapType等）来泛化Dataframe。

假设我有一个类似的架构：

StructType(List(StructField(field1,...), StructField(field2,...), ArrayType(StructType(List(StructField(nested_field1,...), StructField(nested_field2,...)),nested_array,...)))

希望将其改编为具有以下结构的平台：

field1
field2
nested_array.nested_field1
nested_array.nested_field2

仅供参考，寻找Pyspark的建议，但其他风味的Spark也很受欢迎。

Answer 1

这个问题可能有点旧，但对于那些仍在寻找解决方案的人来说，你可以使用select *来内联复杂的数据类型：

首先让我们创建嵌套数据框：

from pyspark.sql import HiveContext
hc = HiveContext(sc)
nested_df = hc.read.json(sc.parallelize(["""
{
  "field1": 1, 
  "field2": 2, 
  "nested_array":{
     "nested_field1": 3,
     "nested_field2": 4
  }
}
"""]))

现在要压扁它：

flat_df = nested_df.select("field1", "field2", "nested_array.*")

您可以在此处找到有用的示例： https://docs.databricks.com/delta/data-transformation/complex-types.html

如果您有太多嵌套数组，可以使用：

flat_cols = [c[0] for c in nested_df.dtypes if c[1][:6] != 'struct']
nested_cols = [c[0] for c in nested_df.dtypes if c[1][:6] == 'struct']
flat_df = nested_df.select(*flat_cols, *[c + ".*" for c in nested_cols])

Answer 2

我已经开发了一种递归方法来展平任何嵌套的DataFrame。该实现在AWS Data Wrangler项目上进行：

import awswrangler    

session = awswrangler.Session(spark_session=spark)
dfs = session.spark.flatten(dataframe=df_nested)
for name, df_flat in dfs.items():
    print(name)
    df_flat.show()

或check the sources查看原始实现。

Answer 3

Here's my final approach:

1) Map the rows in the dataframe to an rdd of dict. Find suitable python code online for flattening dict.

flat_rdd = nested_df.map(lambda x : flatten(x))

where

def flatten(x):
  x_dict = x.asDict()
  ...some flattening code...
  return x_dict

2) Convert the RDD[dict] back to a dataframe

flat_df = sqlContext.createDataFrame(flat_rdd)

Answer 4

这将拼合具有结构类型和数组类型的嵌套df。通过Json读取数据时通常会有所帮助。对此https://stackoverflow.com/a/56533459/7131019

进行了改进

from pyspark.sql.types import *
from pyspark.sql import functions as f

def flatten_structs(nested_df):
    stack = [((), nested_df)]
    columns = []

    while len(stack) > 0:
        
        parents, df = stack.pop()
        
        array_cols = [
            c[0]
            for c in df.dtypes
            if c[1][:5] == "array"
        ]
        
        flat_cols = [
            f.col(".".join(parents + (c[0],))).alias("_".join(parents + (c[0],)))
            for c in df.dtypes
            if c[1][:6] != "struct"
        ]

        nested_cols = [
            c[0]
            for c in df.dtypes
            if c[1][:6] == "struct"
        ]
        
        columns.extend(flat_cols)

        for nested_col in nested_cols:
            projected_df = df.select(nested_col + ".*")
            stack.append((parents + (nested_col,), projected_df))
        
    return nested_df.select(columns)

def flatten_array_struct_df(df):
    
    array_cols = [
            c[0]
            for c in df.dtypes
            if c[1][:5] == "array"
        ]
    
    while len(array_cols) > 0:
        
        for array_col in array_cols:
            
            cols_to_select = [x for x in df.columns if x != array_col ]
            
            df = df.withColumn(array_col, f.explode(f.col(array_col)))
            
        df = flatten_structs(df)
        
        array_cols = [
            c[0]
            for c in df.dtypes
            if c[1][:5] == "array"
        ]
    return df

flat_df = flatten_array_struct_df(df)

**

Answer 5

以下要点将使嵌套json的结构变平，

import typing as T

import cytoolz.curried as tz
import pyspark


def schema_to_columns(schema: pyspark.sql.types.StructType) -> T.List[T.List[str]]:
    """
    Produce a flat list of column specs from a possibly nested DataFrame schema
    """

    columns = list()

    def helper(schm: pyspark.sql.types.StructType, prefix: list = None):

        if prefix is None:
            prefix = list()

        for item in schm.fields:
            if isinstance(item.dataType, pyspark.sql.types.StructType):
                helper(item.dataType, prefix + [item.name])
            else:
                columns.append(prefix + [item.name])

    helper(schema)

    return columns

def flatten_frame(frame: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:

    aliased_columns = list()

    for col_spec in schema_to_columns(frame.schema):
        c = tz.get_in(col_spec, frame)
        if len(col_spec) == 1:
            aliased_columns.append(c)
        else:
            aliased_columns.append(c.alias(':'.join(col_spec)))

    return frame.select(aliased_columns)

然后您可以将嵌套数据展平为

flatten_data = flatten_frame(nested_df)

这将为您提供扁平的数据框。

要点取自https://gist.github.com/DGrady/b7e7ff3a80d7ee16b168eb84603f5599

展平嵌套的Spark Dataframe

5 个答案: